8.1.1. Integer Types

The types smallint, integer, and bigint store whole numbers, that is, numbers without fractional components, of various ranges. Attempts to store values outside of the allowed range will result in an error.

The type integer is the usual choice, as it offers the best balance between range, storage size, and performance. The smallint type is generally only used if disk space is at a premium. The bigint type should only be used if the integer range is not sufficient, because the latter is definitely faster.

The bigint type may not function correctly on all platforms, since it relies on compiler support for eight-byte integers. On a machine without such support, bigint acts the same as integer (but still takes up eight bytes of storage). However, we are not aware of any reasonable platform where this is actually the case.

SQL only specifies the integer types integer (or int) and smallint. The type bigint, and the type names int2, int4, and int8 are extensions, which are shared with various other SQL database systems.

8.1.2. Arbitrary Precision Numbers

The type numeric can store numbers with up to 1000 digits of precision and perform calculations exactly. It is especially recommended for storing monetary amounts and other quantities where exactness is required. However, arithmetic on numeric values is very slow compared to the integer types, or to the floating-point types described in the next section.

In what follows we use these terms: The scale of a numeric is the count of decimal digits in the fractional part, to the right of the decimal point. The precision of a numeric is the total count of significant digits in the whole number, that is, the number of digits to both sides of the decimal point. So the number 23.5141 has a precision of 6 and a scale of 4. Integers can be considered to have a scale of zero.

Both the maximum precision and the maximum scale of a numeric column can be configured. To declare a column of type numeric use the syntax

NUMERIC(precision, scale)

The precision must be positive, the scale zero or positive. Alternatively,

NUMERIC(precision)

selects a scale of 0. Specifying

NUMERIC

without any precision or scale creates a column in which numeric values of any precision and scale can be stored, up to the implementation limit on precision. A column of this kind will not coerce input values to any particular scale, whereas numeric columns with a declared scale will coerce input values to that scale. (The SQL standard requires a default scale of 0, i.e., coercion to integer precision. We find this a bit useless. If you're concerned about portability, always specify the precision and scale explicitly.)

If the scale of a value to be stored is greater than the declared scale of the column, the system will round the value to the specified number of fractional digits. Then, if the number of digits to the left of the decimal point exceeds the declared precision minus the declared scale, an error is raised.

Numeric values are physically stored without any extra leading or trailing zeroes. Thus, the declared precision and scale of a column are maximums, not fixed allocations. (In this sense the numeric type is more akin to varchar(n) than to char(n).) The actual storage requirement is two bytes for each group of four decimal digits, plus eight bytes overhead.

In addition to ordinary numeric values, the numeric type allows the special value NaN, meaning "not-a-number". Any operation on NaN yields another NaN. When writing this value as a constant in a SQL command, you must put quotes around it, for example UPDATE table SET x = 'NaN'. On input, the string NaN is recognized in a case-insensitive manner.

The types decimal and numeric are equivalent. Both types are part of the SQL standard.

8.1.3. Floating-Point Types

The data types real and double precision are inexact, variable-precision numeric types. In practice, these types are usually implementations of IEEE Standard 754 for Binary Floating-Point Arithmetic (single and double precision, respectively), to the extent that the underlying processor, operating system, and compiler support it.

Inexact means that some values cannot be converted exactly to the internal format and are stored as approximations, so that storing and printing back out a value may show slight discrepancies. Managing these errors and how they propagate through calculations is the subject of an entire branch of mathematics and computer science and will not be discussed further here, except for the following points:

• If you require exact storage and calculations (such as for monetary amounts), use the numeric type instead.

• If you want to do complicated calculations with these types for anything important, especially if you rely on certain behavior in boundary cases (infinity, underflow), you should evaluate the implementation carefully.

• Comparing two floating-point values for equality may or may not work as expected.

On most platforms, the real type has a range of at least 1E-37 to 1E+37 with a precision of at least 6 decimal digits. The double precision type typically has a range of around 1E-307 to 1E+308 with a precision of at least 15 digits. Values that are too large or too small will cause an error. Rounding may take place if the precision of an input number is too high. Numbers too close to zero that are not representable as distinct from zero will cause an underflow error.

In addition to ordinary numeric values, the floating-point types have several special values:

Infinity
-Infinity
NaN

PostgreSQL also supports the SQL-standard notations float and float(p) for specifying inexact numeric types. Here, p specifies the minimum acceptable precision in binary digits. PostgreSQL accepts float(1) to float(24) as selecting the real type, while float(25) to float(53) select double precision. Values of p outside the allowed range draw an error. float with no precision specified is taken to mean double precision.

8.1.4. Serial Types

The data types serial and bigserial are not true types, but merely a notational convenience for setting up unique identifier columns (similar to the AUTO_INCREMENT property supported by some other databases). In the current implementation, specifying

CREATE TABLE tablename (
    colname SERIAL
);

is equivalent to specifying:

CREATE SEQUENCE tablename_colname_seq;
CREATE TABLE tablename (
    colname integer DEFAULT nextval('tablename_colname_seq') NOT NULL
);

Thus, we have created an integer column and arranged for its default values to be assigned from a sequence generator. A NOT NULL constraint is applied to ensure that a null value cannot be explicitly inserted, either. In most cases you would also want to attach a UNIQUE or PRIMARY KEY constraint to prevent duplicate values from being inserted by accident, but this is not automatic.

To insert the next value of the sequence into the serial column, specify that the serial column should be assigned its default value. This can be done either by excluding the column from the list of columns in the INSERT statement, or through the use of the DEFAULT key word.

The type names serial and serial4 are equivalent: both create integer columns. The type names bigserial and serial8 work just the same way, except that they create a bigint column. bigserial should be used if you anticipate the use of more than 2³¹ identifiers over the lifetime of the table.

The sequence created for a serial column is automatically dropped when the owning column is dropped, and cannot be dropped otherwise. (This was not true in PostgreSQL releases before 7.3. Note that this automatic drop linkage will not occur for a sequence created by reloading a dump from a pre-7.3 database; the dump file does not contain the information needed to establish the dependency link.) Furthermore, this dependency between sequence and column is made only for the serial column itself. If any other columns reference the sequence (perhaps by manually calling the nextval function), they will be broken if the sequence is removed. Using a serial column's sequence in such a fashion is considered bad form; if you wish to feed several columns from the same sequence generator, create the sequence as an independent object.